System and method for a personalized reminder with intelligent self-monitoring

ABSTRACT

The system and method disclosed utilizes a wearable device to collect data generated pursuant to user&#39;s kinesthetic movement. The instant innovation filters and cleans the data, then slices the data into subsets. The subsets are analyzed with a Machine Learning algorithm to identify signal patterns indicative of statistically significant behavioral metrics, and the instant innovation returns insights based in part on relationships between said behavioral metrics. These insights are returned to an observer in the form of a report.

CLAIM TO PRIORITY

This application claims under 35 U. S.C. § 120, the benefit of theapplication Ser. No. 15/898,492, filed Feb. 17, 2018, titled “Apparatusand Method for a Personalized Reminder with IntelligentSelf-Monitoring,” which is hereby incorporated by reference in itsentirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure, as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND

Wearable systems that incorporate sensors to determine physicalparameters of a wearer are known and present in the marketplace. Sensorsindicating position, heartrate, movement in exercise positions, amongother parameters collect data about the user and provide feedback to auser in realtime. Such systems may also be connected through a datacommunications channel to a computer system having analytical softwareto review collected data and provide analysis to a user or third partyon parameters that are of interest to the user. With appropriatelyminiaturized electronics, the sensors may be located in a smallerportion of the user's body such as the ear. The data collected may beused to assist users understanding about their physical state duringexercise, work, sleep, or other activities.

Feedback may also be provided to the user through a wearable device. Thefeedback may be through elements installed within the wearable device ormay be sent to a mobile or WIFI connected device that is in datacommunication with the wearable device. The feedback may form a portionof a user's medical record, or may be used to assist the wearer inkeeping physical parameters within certain specified ranges duringphysical activity.

The wearable device may also be active to determine whether a user wasengaged in undesirable behavior while the device is being worn. Thewearable device may actively monitor the user to collect and storeinformation about the user's activities at certain time periods and/orwhen the user's activity level exceeds pre-configured thresholds forspecified activities. If the determined intensity of activity exceedsthe established threshold, the device could activate the feedbackmechanism in an attempt to provide correction for the user to follow soas to change the user's behavior. However, currently available wearabledevices do not often provide the user with the capability to interactwith the feedback capability of the wearable device to providecustomized feedback and corrective capability.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain illustrative embodiments illustrating organization and method ofoperation, together with objects and advantages may be best understoodby reference to the detailed description that follows taken inconjunction with the accompanying drawings in which:

FIG. 1 is a view of a system configuration consistent with certainembodiments of the present invention.

FIG. 2 is an operational flow diagram for pseudo-randomized operationconsistent with certain embodiments of the present invention.

FIG. 3 is an operational flow diagram for prompts incorporated a user'sschedule consistent with certain embodiments of the present invention.

FIG. 4 is an operational flow diagram for detecting fidget behaviorconsistent with certain embodiments of the present invention.

FIG. 5 is an operational flow diagram for a behavior typing sub-processconsistent with certain embodiments of the present invention.

FIG. 6 is an operational flow diagram for a behavior reportingsub-process consistent with certain embodiments of the presentinvention.

DETAILED DESCRIPTION

While this invention is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail specific embodiments, with the understanding that the presentdisclosure of such embodiments is to be considered as an example of theprinciples and not intended to limit the invention to the specificembodiments shown and described. In the description below, likereference numerals are used to describe the same, similar orcorresponding parts in the several views of the drawings.

The terms “a” or “an”, as used herein, are defined as one or more thanone. The term “plurality”, as used herein, is defined as two or morethan two. The term “another”, as used herein, is defined as at least asecond or more. The terms “including” and/or “having”, as used herein,are defined as comprising (i.e., open language). The term “coupled”, asused herein, is defined as connected, although not necessarily directly,and not necessarily mechanically.

Reference throughout this document to “one embodiment”, “certainembodiments”, “an embodiment” or similar terms means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the presentinvention. Thus, the appearances of such phrases or in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments without limitation.

Reference throughout this document to “On-task Behavior”, or similarterms “Focused Behavior”, “Focus”, or “Attention” refers to anybehaviors deemed appropriate or desired by the user in the context ofusing a reminder device. In a non-limiting example, if a user desires todemonstrate better attention, listening skills or work completion, thesebehaviors may be deemed as “On-Task Behavior”.

Reference throughout this document to “Off-task Behavior” refers tobehaviors deemed inappropriate or not desired by the user in the contextof using a reminder device.

Reference throughout this document to “Fidgeting” or “Fidget behavior”,refers to how frequently an individual tends to shuffle, wiggle orengage in other ‘still or seated movements’, commonly seen in childrenand adults with Attention Deficit, Hyperactivity Disorder, alsoincluding but not limited to wrist and hand movements, and includinghand flapping, hand-wringing and other stereotyped behaviors commonlyassociated with Autism stimming, that may affect the person's productivebehavior and/or be related to their ability to quietly sit or stand andattend or focus on a given task.

In an embodiment with regard to these reported behaviors, users of theinvention described in this document may utilize a well-known techniquecalled Self-Monitoring, or SM. Self-Monitoring is a well-documented,highly researched behavioral intervention which has been shown to be ahighly effective means of increasing desired behaviors and decreasingundesired behaviors. Previously, however, there has been a lack ofintegration between SM and technology. This document presents a systemand method to operatively utilize SM to improve the on-task behavior,focus and/or attention of a user by precisely adjusting factors thatadjust the frequency, intensity and patterning of reminder promptsthrough various stimuli, such as a tactile vibration, audible tone,visual stimulus, or other stimuli that may form a reminder prompt,defined as, in a non-limiting example, “F-factors”.

In this embodiment, the system and method utilizes a pool ofresearch-based data tables to pseudorandomize meta-cognitive reminders.Such reminders encourage users to be more cognizant and aware of theirown behaviors. This self-awareness of personal behaviors may in-turnhelp increase an individual's ability to stay on-task. By providing aplatform to query a user regarding their own on-task and off-taskbehaviors, as well as to collect and compile in order to intelligentlyharness this data, the system and method described may be able to impartchange through targeted reminders driven by actual, longitudinalcollected user data.

In an embodiment, an algorithm may determine when and how to adjustreminders based-on user input in regards to self-reported on-task andoff-task behaviors. Over time, this system progressively gathers userdata and learns details of a user's activity and habits, and becomesmore and more attuned to the user's needs. The device containing thesensors and that provides prompt reminders to a user, is worn by theuser. In a non-limiting example, the device may sample the user'sbehavior multiple times per second, collecting measurements from eachsampling period to aggregate data over time for analysis, operations,and to provide a prompt to the user when the system determines that anychange that merits a reminder to the user has occurred. Based-oncollected and historically aggregated data that illumine user behaviors,a computer software module may be operative to analyze the currentcollected and historically aggregated data to determine the mostappropriate minimum and maximum values for prompt timing to ensuremaximum on-task behavior with a high degree of confidence in the resultsof the analysis. The data analysis may also serve to minimizeover-prompting to keep meaningfulness high, where meaningfulness isdefined as of benefit to a user in accomplishing a desired goal, andhabituation to repeated prompting low.

In an embodiment, while collecting data, it is imperative to categorizethe collected information into useful segments. If not properlycategorized, the data may become less relevant due to situationalconstraints. In a non-limiting example, if an individual desires toactively track the ability to listen to a teacher or lesson, but fromtime to time is sitting at a cafeteria table during lunch instead ofbeing engaged in listening to a teacher or lesson, then the informationcollected is less relevant. Even different environments that are morerelevant tend to net differing levels of interest, attention andon/off-task behaviors. For this reason, a scheduling component isincorporated into the system and method to intelligently collect andorganize data. In a non-limiting example, data will not be collectedduring certain, pre-determined periods of time, such as during lunch,physical education, or other time periods as configured during theoperation setup of the device in which the user may be distracted or notdedicated to a desired task, but may be collected and then leveraged ina unique fashion during other time periods. In a non-limiting example,reminders may also be amplified, as needed, in certain key environments.To do this, a calendar or schedule is initially completed by the user,including start and end times to provide the system with insight into auser's established schedule. The reminder device component of the systemincludes a real-time clock feature which auto-updates in order tocorrectly utilize schedule events and engage reminders.

In an embodiment, the system and method may actively collect a user'sreported behaviors in order to continually optimize each of severalfocus factors, previously defined as “F factors”. Based on a user'sreported behaviors to a posed question, such as “When you feel thisvibrate, ask yourself if you were on-task or off-task—press userresponse button 2 times for on-task and 1 time for off-task”, a computeralgorithm is operative to determine whether or not to increase, decreaseor leave unchanged the aforementioned F-factors in order to continuallyprovide as much prompting as necessary to improve users on-taskbehaviors, but as little prompting so as not to become burdensome or toforce user to habituate. Habituation is further staved-off bypseudo-randomizing one or more of the F factors.

In an exemplary embodiment, the system and method described comprises adevice, preferably worn on the wrist although this should in no way beconsidered limiting as the device may be worn on any appendage or as anecklace, headband, or other fashion-conscious garment. The device mayhave a processor that incorporates a machine learning algorithm todetermine alternations in vibrations and changes in the pitch ofgenerated tones to provide reminders to a wearer to remain focused on anactivity. In this exemplary embodiment, the machine learning algorithmis operative to determine the best times to remind the wearer based uponuser action in response to the user history of responses to reminderprompts. The machine learning algorithm may also take into account theuser's daily or weekly schedule of activities and tasks in determiningwhen to send a reminder prompt. The wearer's schedule may be added toprovide more directed collection of data with regard to on/off task timepercentage, goals of the user, and sending an automatic email report onuser activity to a caregiver, parent, health care professional, or otherauthority associated with the user.

In an alternative exemplary embodiment, a wearer, in some instances achild, may receive additional mandatory remind times when they mustrespond to a device vibration and/or tone by tapping the device. Devicevibrations may indicate states or timing through alternative vibrations,tones, or other distinguishing tactile or audible actions. The machinelearning algorithm associated with the device may modify vibrations,tones, sequences, intensity and other prompt actions based upon userresponse. The vibrations and frequencies of the tones may change basedupon patterns of response from the user, thus learning the habits of theuser in terms of response to prompts.

In an embodiment, the instant innovation includes a wearable device usedto collect data generated pursuant to user's kinesthetic movement. Theinstant innovation filters and cleans the data, then slices the datainto subsets. The subsets are analyzed with a Machine Learning algorithmto identify signal patterns indicative of statistically significantbehavioral metrics, and the instant innovation returns insights based inpart on relationships between said behavioral metrics. These insightsare returned to an observer in the form of a report.

In an embodiment, the device may be in communication with a systemserver for reporting, updates, and other communications. A detectionengine may receive sensor data from the device and may filter thereceived data for desired potential data while discarding datadetermined to be noise or otherwise unwanted data. The instantinnovation may utilize an on-board quantization processor, in anon-limiting example, to send desired data to and/or from one or moremobile devices. The instant innovation may utilize a communicationsprotocol such as, but not limited to, Bluetooth as incorporated into adevice to send such desired data to and/or from one or more mobile orsmart devices. In an embodiment, the instant innovation may employ abase server, such as, by way of non-limiting example, a Lambda Data BaseServer to slice the desired data into smaller data chunks for ease ofprocessing and analysis. The instant innovation may store a copy of alldesired data upon one or more servers.

The innovation described herein may use one or more Machine LearningAlgorithms associated with one or more processors to scan and analyzethe desired data for signal patterns that help the instant innovation toidentify kinesthetic movement that may characterize any cyclic,repetitive non-academically functional behavior associated with ADHD,Autism, or other motor-related medical conditions. In a non-limitingexample, such cyclic, repetitive non-academically functional behaviorsmay be quantified or identified as fidget behaviors. In an embodimentthe instant innovation identifies and distinguishes signal patternsassociated with a user's sitting and standing behavior to determinewhether cyclic, repetitive non-academically functional behaviors, suchas, in a non-limiting example, fidget behaviors are present. Signalpatterns associated with a user's sitting are distinguished betweenfidgeting and non-fidgeting behavior. The instant innovationcharacterizes analyzed sitting fidgeting behavior as one or more ofseveral distinctive types of behavior including, by way of non-limitingexample, drumming, jumping, tapping, hopping, and/or rocking among otherkinesthetic movements.

In an embodiment, the characterized behavior may also include specificnon-limiting categories including handwriting, in which the innovationdetermines the rate at which the user writes words, which, in anon-limiting example, may be measured as words written per minute, ormay be quantified in any other manner that determines the rate ofcompleted words in a given period of time. The system may also quantifysuch motions as hand raising, in which the innovation determines therate at which the user raises a hand during a given time period.Additionally, the system may quantify turning behavior, in which theinnovation determines the rate at which the user turns to speak orotherwise interact with peers sitting nearby, where the rate may bequantified as the number of interactions in a given period of time.

Signal patterns associated with a user's standing fidget behavior aredistinguished by identifying when a user is standing still or walkingwhile standing. The system may also quantify when a user is running asopposed to standing behaviors. When the instant innovation as describedherein determines that standing behavior is characterized by walking orrunning, the instant innovation performs and stores one or more stepcounts, as well as collecting information on when a user starts andstops either walking or running, and stores this collected data forlater analysis.

The system may also collect and share information such as the overall‘fidget minutes’ captured during a given time period and store thisinformation as metrics on the actions of a user during periods ofrecorded fidget behavior. During periods when fidget behavior has beendetected, the fidget behavior actions and measured data that arecaptured may include all fidget behavior metrics in either standing orseated positions, and a count of steps and/or indication of caloriesexpended during walking, running, or other standing or sitting fidgetbehavior.

In additional embodiments, the device may track additional kinestheticmovement where an individual is largely stationary, such as in a seatedposition. Such stationary movement (fidgeting, etc.) data may becollected and tracked to identify and quantify the user's various seatedbehaviors in addition to any standing fidget behaviors. Trackingfidgeting behaviors of all types permits the system and method of theinstant innovation to perform updates and changes to the promptgeneration algorithm based upon determining various states of physicalexcitation and activity, and/or of physical response or reaction time,as well as to situations in which the user does not respond to a prompt.In additional embodiments, the system and method may modify the promptgeneration algorithm based upon a user's class scheduling, if the useris a student, or based upon the previous day and/or week's performanceon responses to prompts captured and stored in the aggregated historydata. The system may analyze the captured data to determine prompts tosend that may assist a user in modifying or minimizing fidget behaviorsof all types and transmit such modified and updated prompts to thewearable device for action by the user.

In an embodiment, the system also utilizes trends in individual user'sbehaviors to prescribe suggestions, by combining research-basedinterventions with actual user behaviors measured over time, for theduration and intensity of said research-based behavioral interventions.This allows information to be presented to one or more observersincluding teachers, supervisors, other authority figures, or the usersthemselves with individualized recommendations for each user in eachenvironment such as classes, job functions, or any other environment inwhich performance is expected and required.

In an embodiment, the instant innovation enables a personalized insightengine by creation, maintenance and analysis of one or more individualuser databases. The instant innovation performs multiple queries forspecific metrics above key P-values. As defined herein, a P-valuerepresents the probability of obtaining results at least as extreme asthe observed results of a statistical hypothesis test, assuming the nullhypothesis is correct. In instances where the algorithm of the instantinnovation determines that the presence of specific metrics above keyP-values satisfies criteria for one or more specific behavioralrelationships, the instant innovation may create and send one or morereports to the user, teacher, supervisor or other observer. Such one ormore reports may be auto-generated text descriptions revealing one ormore behavioral insights that may be ascribed to the user of the device.In a non-limiting example, such text description may read, “It was notedthat when Child 3 has the opportunity to take at least 44,000 stepsbefore Tuesday in a given week, Child 3's hourly rate of Fidgeting forthe rest of the week has been shown to decrease by 33 percent on averagewhen compared to weeks when they've not had this opportunity.” This textmay be transmitted to the user, teacher, doctor or clinician, or otherobserver if the insight engine algorithm of the instant innovation hasdetermined that sufficient criteria have been met to enable thetransmission of such insights based upon analysis of the individual userdatabase. In other non-limiting examples, the instant innovation mayreturn a text description such as, “It was noted that when Child 2 hasthe opportunity to take at least 15,000 steps before lunch each day,Child 2's Attention Span rate for the rest of the day has been shown toimprove by 5 minutes on average when compared to days when they've nothad this opportunity,” or “It was noted that when Child 1 has theopportunity to fidget for at least 15 minutes before 10 am each day,Child 1's Focus Rate for the rest of the day has been shown to be 30%higher than on days when they've not had this opportunity.”

Walking/Running/Fidgeting are also quantified by advanced motion sensorsand tracked over time in order to determine how active an individual maybe, and whether or not their activity level is considered ‘over-active’relative to both self and peers. The determination of activity levelbased upon these activities may then prompt an increase in the frequencyof reminders or in the duration or composition of reminders, asnecessary, based-on excessive levels of fidgeting as described above, toremind users to re-engage in their desired on-task behaviors.

The system implements all data collected (Self-Monitored responses,physical motions, schedule and environment) to make real-time on-the-flychanges to reminder prompt durations, frequency, amplitude andwavelength to both custom-tailor to the user's exact needs, so as not toover, or under, prompt, as well to provide a constantly unique promptexperience, which may help increase time on-task and reduce habituation.

Turning now to FIG. 1 , this figure presents a view of a systemconfiguration consistent with certain embodiments of the presentinvention. In an exemplary embodiment, the system and method comprises adevice 100 that is worn by a user. One or more sensors, including butnot limited to motion sensors, may be incorporated into the device tocollect motion data associated with the user. One or more promptelements, including, but not limited to, elements that produce tactile,visual, auditory, or other prompts to catch the attention of the userare also included in the device 100. The device 100 may also incorporatea display element 102 that provides for visual information to bepresented to the user at the determination of the system. The device 100may also contain an RF, WIFI, Bluetooth, Bluetooth Low Energy (BLE), orother transmission protocols developed and released for use in wired orwireless data communication.

In an embodiment, the transmission capability may provide for connectionand communication with a system server 104. The system server 104 mayincorporate a plurality of software modules (not shown) operative totransmit commands and data to the device 100 and receive data from thedevice 100. The software modules may transmit prompt commands to thedevice 100, stimulating any of the tactile, visual, auditory, or otherprompt elements to activate and provide a prompt to capture the user'sattention. The user will sense the prompt and provide a response to theprompt by tapping or otherwise interacting with the device 100 toindicate whether the user is on-task or off-task at the time the promptwas noticed by the user. The response by the user may then betransmitted by the device 100 back to the server 104 where the userresponse may be stored in a database element 106 in a file dedicated tothe user and containing all response and tracking data associated witheach user. The server 104 may then communicate a report to a display orother interactive device 108 associated with a parent, teacher,healthcare professional, or other authority figure associated with theuser. This informative report can be used to both inform as well asmotivate the user by providing customized reporting on progress towardsbehavioral improvement, personalized goals or peer-based benchmarks;this can be presented to the user in the form of a customized report,animation/cartoon character, avatar or other modality. Progress fromsuccessful feedback to SM prompts may also be used to generate ‘points’,tokens, credits or the like in order to motivate the user by allowingthem additional time, features, etc. for an in-application game, or agame external from the device application. The device and companionsoftware can also utilize data garnered from the device to driveindividualized recommendations, suggestions, and/or feedback, presentedin a daily, weekly or monthly report.

Turning now to FIG. 2 , this figure presents an operational flow diagramfor pseudo-randomized operation consistent with certain embodiments ofthe present invention. In an exemplary embodiment, the user attaches thedevice to their person and the system is activated by the user at 200.The device interrogates the internal prompt timing value transmittedfrom the server to the device at 202 and checks to determine if theprompt timing value has been exceeded.

At 204 the device has determined that the prompt timing value has beenexceeded and initiates the prompt chosen by the user, whether tactile,auditory, visual, or other prompt indication. At 206 the device waits apre-configured amount of time for a response to the prompt indicationfrom the user. If the device receives a response from the user in thepre-configured amount of time permitted for the user to respond, at 208the device reviews the response from the user to determine if the useris indicating they are on-task or off-task. If the user has indicated bythe appropriate response that they were on-task at the time theyreceived the prompt from the device, the device at 210 sends anindication of on-task behavior to the system server which is then activeto update the user on-task tracking file in the database. If, however,the user has indicated, again by the appropriate response, that theywere off task, the device at 212 sends an indication of off-taskbehavior to the system server which is then active to update the useroff-task tracking file in the database.

At 214, when the user has not provided a response to the prompttransmitted by the device, the device sends an indication of a lack ofresponse to the prompt to the system server which is then active toupdate the user no-response tracking file in the database.

Regardless of the response or lack of response to the prompt by theuser, at 216 the system server is operable to create a new time periodsetting for the next prompt interval to be used by the device byactivating a pseudo-randomization algorithm to calculate a new prompttime period utilizing tracked on and off task time percentage and theuser's goals. At 218, the system server resets the prompt time period inthe internal database and transmits this new time period value to thedevice. The device replaces the previous prompt time interval with thenewly received prompt time interval and begins checking for elapsed timeagainst the prompt time interval. At 220 the system updates the userrecord for goal tracking based upon the user response information.

Turning now to FIG. 3 , this figure presents an operational flow diagramfor prompts incorporated a user's schedule consistent with certainembodiments of the present invention. In an exemplary embodiment, at 300the system server may receive a response from the device associated witha user. At 302 the system server tracking software module may beoperable to calculate the time from the most recent response receivedfrom the user associated with the device. At 304, the system server isoperable to create a new time period setting for the next promptinterval to be used by the device by activating a pseudo-randomizationalgorithm to calculate a new prompt time period utilizing on and offtask time percentage and the user's goals. At 306, the system serverdetermines whether the user's schedule is to be utilized in thecalculation of a new prompt time period. If the user's schedule is to beutilized, at 308 the system server may take into account the user'sschedule, special circumstances, previous response performance, andinput these parameters into the pseudo-randomizer software module. Thepseudo-randomizer module may then utilize these input parameters andinitiate the pseudo-randomization algorithm to modify the response timeperiod by determining the most appropriate minimum and maximum valuesfor prompt timing to ensure maximum on-task behavior, while alsominimizing over-prompting to keep meaningfulness high and habituationlow. The new prompt time period calculated utilizing the user's scheduleis used to update the user tracking information on the system server.

If the user's schedule is not to be used in the calculation of a newprompt time period, the system server simply updates the user trackinginformation with the new prompt time period that was calculated withoutinput from the user's schedule at 310. At 312, the system serverreplaces the elapsed prompt time period with the newly calculated prompttime period and transmits the newly calculated prompt time period to thedevice to replace the prompt time period just elapsed. The system serverthen updates the database with all tracking information and resets thetracking information for the user at 314.

Turning now to FIG. 4 , this figure presents an operational flow diagramfor detecting fidget behavior consistent with certain embodiments of thepresent invention. In this exemplary embodiment, the trackinginformation input by the user may be stored within the wearable device.The system server establishes a data communication connection with thedevice to establish a tracking connection between the device associatedwith a user and the system server at 400. The system server may transmitany tracking information stored within the server to a cloud-basedstorage facility, collect tracking information from the wearable deviceand transmit the collected tracking information to the cloud-basedstorage, or provide a real-time update of tracking information from thewearable device, through the server, and on to the cloud-based storagefacility. The system server at 402 is active to determine that the useris seated by receiving and analyzing motion sensor data to determine theuser's movement. If the user's movement is constrained to within acertain parameter set that indicates the user is not moving to adifferent physical position, not walking or running, the system servermay determine that the user is seated. In an alternative embodiment, thesystem server at 406 may also be active to determine that a user isstanding, hopping, or remaining in one spot for a period of time butperforming fidgeting-type behaviors.

In an embodiment, the system is active to collect all data associatedwith a user, regardless of physical position, whether standing, seated,reclining, or in active motion. However, if the user is determined to beseated or remaining substantially in a single physical position bysensor data analysis, at 404 the system server is active to categorizecollected data as associated with a localized physical position, eitherseated or standing, and collects all sensor data from the device asindicia of a user's fidgeting behaviors. At 406, the movement softwaremodule is operative to determine if the seated or standing behaviors ofa user are indicative of fidgeting, as defined previously. If thecollected behaviors indicate that the user is fidgeting at 408 thesystem server quantifies the user's movement as fidgeting and stores thesensor data along with an indication of fidgeting behavior and timingdata associated with the length of time the user is exhibiting thisfidgeting behavior. Whether the user's behavior is indicative offidgeting, whether standing or seated, or not, the system server updatesthe tracking information associated with the user at 410. At 412, thesystem server calculates a new prompt time interval through apseudo-randomization algorithm with an added parameter to account forfidgeting behavior and transmits the newly calculated prompt timeinterval to the device associated with the user. At 414, the system isoperative to reset the prompt interval tracking to continue operation.

Turning now to FIG. 5 , an operational flow diagram for a behaviortyping sub-process consistent with certain embodiments of the presentinvention is shown. At 500 the sub-process starts. At 502 the system ofthe instant innovation receives data input from a user device. Such datamay in an embodiment be received from a user-wearable device and mayinclude noise as well as potentially desirable data. At 504 the systemof the instant innovation filters and cleans the data, differentiatingpotentially desirable data from noise. As previously described, thefiltering of the data is performed to identify and remove noisy signalsthat do not contribute to the data, and performs signal analysis toidentify weak and/or missing portions of the received signals tostrengthen and repair the data signals received by the system. Thesystem retains potentially desirable data in one or more data structuresand discards those signals and data fragment which the system determinesare noise and not useful data. The retained potential data may beprocessed by a quantization processor on-board the user device toquantify and normalize the retained data. The output from thequantization processor may then be sent to a base server by any suitablecommunication method or protocol including, but not limited to, a mobiledevice employing a Bluetooth module. The base server may be a LambdaData Base Server, and at 506 the data may be sliced into smaller chunksand the resultant chunked data may be stored on the server. At 508 thesystem of the instant innovation employs a Machine Learning Algorithm toprocess the chunked data. In this chunked data processing, the MachineLearning Algorithm scans the chunked data for signal patterns which maybe determinative of kinesthetic movement that may be characterized asany cyclic, repetitive non-academically functional behavior associatedwith conditions such as ADHD, Autism, or other motor-related medicalconditions. This kinesthetic movement may, in a non-limiting example, beclassified as fidget behavior at 510. The Machine Learning Algorithm mayalso determine if the identified fidget behavior is fidget behavior thatis associated with standing fidget behavior, seated fidget behavior, ora combination of both types of fidget behavior at 512. By way ofnon-limiting example, fidget behavior so identified may include humankinesthetic behavior such as drumming, jumping, tapping, hopping and/orrocking. At 514 the system of the instant innovation returns a report toan observer in combination with recommendations for ways to improveconcentration and on-task behavior by modifying the user's activities tominimize such fidget behavior. At 516 the sub-process ends.

Turning now to FIG. 6 , an operational flow diagram for a behaviorreporting sub-process consistent with certain embodiments of the presentinvention is shown. FIG. 6 illustrates more precisely the steps shown at512 and 514 in FIG. 5 . At 600 the sub-process starts. At 602 the systemof the instant innovation identifies the type of fidget behavior andwhether the behaviors recorded are indicative of seated or standingfidget behaviors. At 604 the system of the instant innovation performsmulti-queries for relationships among observed, cleaned, chunked, andidentified fidget behavior data. System-performed multi-queries searchfor relationships between system-identified metrics that are above keyP-values. If at 608 the system determines that the identified metricsare above such key P-values, then at 610 the system determines thatcertain criteria are met to determine the existence of a specificrelationship. By way of non-limiting example, a specific relationshipcould exist (in a statistically significant way) between one userbehavior and a desired outcome for another user behavior. Consequently,the instant innovation may calculate one or more insights intoconnections between, among, and within the user's various behaviors. Forinstance, the system may be able to determine that a user who takes atleast 15,000 steps before lunch experiences measurably improvedattention span for the remainder of the day. At 612 the systemauto-generates one or more displayable reports intended to convey theexistence of the special relationship and may generate one or moreinsights regarding how the information may be used by the user or anobserver and prompting the user to follow the presented recommendation.At 614 the system sends one or more displayable reports, generated astext, video imagery, or other displayable characterization, to anobserver who may be the user, a teacher, a clinician, a therapist, orother official observer. In an embodiment such displayable reports maybe in other formats including, by way of non-limiting example, audio,visual, or haptic. At 616 the sub-process ends.

While certain illustrative embodiments have been described, it isevident that many alternatives, modifications, permutations andvariations will become apparent to those skilled in the art in light ofthe foregoing description.

We claim:
 1. A computer system for behavior modification of a subjectwith attention-deficit/hyperactivity disorder (ADHD), comprising: a dataserver configured to: receive kinesthetic movement data from a wearabledevice that generates the kinesthetic movement data from movement of asubject; analyze the kinesthetic movement data for signal patterns tocharacterize cyclic, repetitive behavior of the user; analyze through anaction of one or more computerized computational methods said signalpatterns to identify behavioral metrics in said collected data, wheresaid behavioral metrics exceed pre-configured P-values; identifying thatthe behavioral metrics that exceed the P-values satisfy criteria for oneor more specific behavioral relationships; determining behavioralinsights related to the concentration of the subject and the cyclic,repetitive behavior of the subject characterized from the signalpatterns; and cause a display of one or more actions or recommendationsrelated to concentration of the subject based on one or more of thebehavioral insights and the behavioral metrics.
 2. The system of claim1, where the cyclic, repetitive behavior of the user is related tofidget behavior.
 3. The system of claim 2, where the fidget behavior isdrumming, jumping, tapping, hopping, rocking and/or any cyclic,repetitive non-academically functional behavior associated with ADHD. 4.The system of claim 1, where the calculated insights reflect connectionswithin the user's behaviors.
 5. The system of claim 1, where the one ormore actions or recommendations are presented in an auto-generated textmessage.
 6. The system of claim 1, where said computerized computationalmethods comprise a Machine Learning Algorithm operative to analyzereceived data to identify movement classified as fidget behavior.
 7. Amethod for behavior modification, comprising: receiving kinestheticmovement data from a wearable device that generates the kinestheticmovement data from movement of a subject; analyzing the kinestheticmovement data for signal patterns to characterize cyclic, repetitivebehavior of the user; analyzing through an action of one or morecomputerized computational methods said signal patterns to identifybehavioral metrics in said collected data, where said behavioral metricsexceed pre-configured P-values; identifying that the behavioral metricsthat exceed the P-values satisfy criteria for one or more specificbehavioral relationships; determining behavioral insights related to theconcentration of the subject and the cyclic, repetitive behavior of thesubject characterized from the signal patterns; and causing a display ofone or more actions or recommendations related to concentration of thesubject based on one or more of the behavioral insights and thebehavioral metrics.
 8. The method of claim 7, wherein the cyclic,repetitive behavior of the user is related to fidget behavior.
 9. Themethod of claim 7, where the fidget behavior is drumming, jumping,tapping, hopping, rocking and/or any cyclic, repetitive non-academicallyfunctional behavior associated with ADHD.
 10. The method of claim 7,where the calculated insights reflect connections within the user'sbehaviors.
 11. The method of claim 7, wherein the returning thebehavioral metrics and the calculated insights is in the form ofauto-generated text messages.
 12. The method of claim 7, where saidcomputerized computational method is a Machine Learning Algorithmoperative to analyze received data to identify movement classified asfidget behavior.